A comparative study of colortexture

Transcription

1 12th Int. Worshop on Systes, Signas & Iage Processing, Septeber 2005, haida, Greece 205 A coparative study of coortexture iage features Diitris Iaovidis* Departent of Inforatics and Teecounications, University of Athens Panepistiiopois, Iisia, Athens, Greece E-ai: rtsiage@di.uoa.gr *orresponding author Diitris Marouis Departent of Inforatics and Teecounications, University of Athens Panepistiiopois, Iisia, Athens, Greece Stavros Karanis Departent of Inforatics and oputer Technoogy, Technoogica Educationa Institute of Laia 3 rd Od Nationa Road, Laia, Greece Abstract: In this wor we copare two spatia and two waveet-doain feature extraction ethods that have been proposed in the recent iterature for coor-texture cassification. The corresponding coor-texture features, naey the Opponent-oor Loca Binary Pattern distributions, the hroaticity Moents, the Waveet orreation Signatures and the oor Waveet ovariance features, are extracted in RGB, I 1 I 2 I 3, HSV and IE-Lab coor spaces. The cassification tas is reaized by Support Vector Machines. Experients are perfored on two standard datasets coprising of 54 and 68 textures fro the Vistex and the Outex databases respectivey. The resuts show that in ost cases coor enhances texture cassification. Both spatia and waveet features can ead to an accurate representation of coor-textures. The appropriateness of a coor-texture feature extraction ethod has to be deterined by considering the trade-off between the accuracy and the feature space diensionaity needs, as these are iposed by a prospective appication. Keywords: Iage Anaysis, oor Texture Features, Support Vector Machines. Reference to this paper shoud be ade as foows: Iaovidis, D.K., Marouis, D.E., and Karanis, S.A. (2005) A coparative study of coor texture features. Biographica notes: D.K. Iaovidis received his PhD in Inforatics fro University of Athens in He is currenty a Research Feow at the Departent of Inforatics and Teecounications, Athens, Greece. His current research interests incude iage anaysis, artificia inteigence, and bioedica systes. D.E. Marouis is an Assistant Professor of Inforatics at the University of Athens, Greece. He received his PhD in Inforatics, MSc in Eectronic Autoation, and MSc in Radioeectricity fro the University of Athens, Greece. His research focuses on reatie, iage/signa processing and bioedica systes. S.A. Karanis is an Associate Professor of Inforatics at the Technoogica Educationa Institute of Laia, Greece. He received his PhD in Inforatics, and BSc in Matheatics fro the University of Athens, Greece. His research focuses on waveet transfor, pattern recognition, iage processing appications, statistica earning ethods for cassification. 1 INTRODUTION oor texture anaysis is a ajor fied of deveopent for new ethods concerning various vision appications. Athough texture perception is not directy associated with coor perception, a variety of coor-texture feature extraction ethods have been proposed in the iterature. Recent approaches to coor-texture anaysis focus on the opyright 2005 Inderscience Enterprises Ltd.

2 206 D. K. Iaovidis, D. E. Marouis, and S.A Karanis expoitation of both intra- and inter-channe inforation, such as the estiation of chroatic correation features (Paschos, 1998), waveet correation signatures (WS) (Van De Wouwer et a., 1999), features quantifying the correation of Gabor features between the iage coor channes (Jain and Heaey, 1998), correation estiates of Zernie oents between different iage coor channes (Wang and Heaey, 1998; A-Rawi and Yang, 2001) distributions of Opponent oor Loca Binary Pattern (O) features (Mäenpää, 2003) and oor Waveet ovariance (W) features (Iaovidis et a., 2004). The effectiveness of the waveet transfor for texture representation, has been pointed out in any studies (Iaovidis et a., 2004; Unser and Eden, 1989; Randen and Husøy, 1999). Nevertheess, any researchers sti persist in supporting the use of spatia-doain features (Paschos, 1998; Mäenpää, 2003; Paschos, 2000). The issue of which approach is the best for the cassification of coor textures has not been thoroughy investigated in the iterature. oparative studies have focused on issues such as the enhanceent introduced by the incusion of coor in texture anaysis (Dribarean and Whean, 2001), the effect of using perceptuay unifor coor spaces for coor texture representation (Paschos, 2001) and whether coor and texture inforation shoud be considered jointy or separatey (Mäenpää and Pietiäinen, 2004). In this paper, we copare recent spatia and waveetdoain coor-texture feature extraction ethods under the experienta fraewor described in (Ojaa et a., 2002), using both Vistex and Outex textures. These incude the extraction of, M, WS and W features. The cassification tas has been assigned to Support Vector Machines (SVM) as these have proved to be robust, resistant to the curse of diensionaity. Moreover, SVMs are ess epirica as regards the deterination of their paraeters copared to standard neura networs and they have proved to provide better generaization perforance than other traditiona cassifiers in any appications, incuding the cassification of textures (Li et a., 2003). The rest of this paper is organized in three sections. Section 2 incudes a brief description of the feature extraction ethods copared. The experienta fraewor and resuts are presented in Section 3. Finay, Section 4 suarizes the concusions of this study. 2 FEATURE EXTRATION METHODS 2.1 Opponent-oor Loca Binary Pattern distributions The Loca Binary Pattern () ethod has been proposed by Ojaa and Pietiainen (1998), as a two-eve version of the texture spectru ethod which uses three eves (0, 1 and 2) for the representation of oca texture patterns. The oca binary pattern of a 33-pixe neighborhood is estiated as foows: 1. The origina 33 neighborhood is threshoded to two eves (0 and 1) using the vaue of the center pixe. 2. The vaues of the pixes in the threshoded neighborhood are utipied by certain weights assigned to the corresponding pixes. 3. The vaues of the eight pixes are sued to obtain a singe vaue for the corresponding pattern. The feature vectors are fored by the histogra bins of the vaues distribution in an iage region. is usuay cobined with a contrast easure which is defined as the difference between the average intensity of 1 pixes and the average intensity of 0 pixes ( features). An extension of the ethod for coor iages, naed, has been proposed by Mäenpää (2003) and invoves the appication of the operator on each coor channe separatey. In addition, each pair of coor channes is used in coecting opponent coor patterns so that the center pixe for a neighborhood and the neighborhood itsef are taen fro different coor channes. In tota, 3 intrachanne histogras (one histogra for each coor channe i, i = 1, 2, 3) and 6 inter-channe histogras are extracted and concatenated into a singe distribution. 2.2 hroaticity oents The hroaticity Moents (Ms) have been proposed by Paschos (2000) as a sipe and coputationay ow-cost ethod for coor texture cassification. Let X, Y, Z quantities represent the XYZ coor space channes. The chroaticity trace T(x, y) and its distribution D(x, y) for an iage of L x, L y diensions are defined as foows: 1, ( i, j) : I( i, j) ( x, y) T( x, y) (1) 0, ese Dxy (, ) (2) where 0 i L x, 0 j L Y, = # of pixes producing (x, y). The functions T(x, y) and D(x, y) are characterized by a set of oents, which for the feature vector and are defined as foows: M M T D (, ) (, ) Xs 1Ys1 x0 y0 x Xs 1Ys1 x0 y0 x y T ( x, y) y D( x, y) where p=+=1, 2, represents their order and X S, Y S are the discrete diensions of the x-y space. The discrete X S Y S space is produced by rescaing and discretization: x = 100x, y = 100y Waveet Energy features The Discrete Waveet Transfor (DWT) of a gray-eve iage is reaized by convoving the iage with a ow pass fiter L and a high pass fiter H, the output of which is then sub-saped dyadicay. This procedure produces a owresoution iage B 0 () and detai iages B j (), j = 1, 2, 3 at scae. The repetition of this fitering procedure for = 1, 2, K resuts in a uti-scae representation of the iage. By oitting the sub-saping operation, a variation of the (3) (4)

3 A oparative Study Of oor-texture Iage Features 207 DWT, the Discrete Waveet Frae Transfor (DWFT) is produced. DWFT is a redundant representation that eads to a texture description toerant to transation (Unser and Eden, 1989). The Waveet Energy (WE) features are estiated by suing the squares of a b coefficients of the output iages B j (), j = 0, 1, 2, 3: E i b (i) 2.4. Waveet orreation Signatures 2 The WS have been proposed by Van De Wouwer et a. (1999) as extensions of the DWFT energy features that tae into account the correation of the waveet coefficients between the iage coor channes. They are derived fro the foowing equation: W E b, ) i B j ( E j b, j b, (5) ( i) b ( i (6) ) E where and are the coefficients of the detai iages Bj(), j = 1, 2, 3, = 1, 2, K of the coor channes and, = 1, 2, 3, = 1, 2, 3 respectivey oor Waveet ovariance The oor Waveet ovariance (W) features are covariance estiates of the 2 nd order statistica inforation inherent in the DWFT of the coor channes of an iage (Iaovidis et a., 2004). The iage coor channes are transfored to the waveet doain by the DWFT. The 2 nd - order statistica inforation of the waveet coefficients is captured by eans of co-occurrence atrices. ooccurrence atrices encode the gray-eve spatia dependence based on the estiation of the 2 nd -order joint conditiona probabiity density function f(i, d, ), which is coputed by counting a pairs of pixes at distance d having gray-eves i and j at a given direction. The anguar dispaceent of d = 1 is incuded in the range of the -vaues {0, /4, /2, 3/4}. B Let M () j i (a) be a cooccurrence atrix estiated over a detai iage Bj(), j = 1, 2, 3, =1, 2, K, of the coor channe i, i = 1, 2, 3, for a direction {0, /4, /2, 3/4}. Four representative statistica features are estiated over each detai iage B j (), j = 1, 2, 3, = 1, 2, K, naey the anguar second oent (f 1 ), the correation (f 2 ), the inverse difference oent (f 3 ) and the entropy (f 4 ). The resuting set of features that correspond to the different coor channes i is F a i, where i = 1, 2, 3, j = 1, 2, 3, = 1, 2, K, F {f1, f 2, f 3, f 4 } and {0, /4, /2, 3/4}. The oor Waveet ovariance of a Feature F (W), between the detai iages B j (), j = 1, 2, 3, = 1, 2, K of coor channes and, = 1, 2, 3, = 1, 2, 3 is estiated as foows:, ov F, W F, (7) 3 RESULTS The experienta evauation of the four coor-texture feature extraction ethods was perfored by using two standard pubicy avaiabe coor-texture datasets fro the Outex database. The coparative study was based on the fraewor proposed in (Ojaa et a., 2002). The first dataset originates fro the VisTex database (ontrib_t_00006) and consists of 54 texture iages of pixe diensions. Each iage was spit into 16 non-overapping sub-iages, foring a tota of 864 sapes. Haf of these sapes were used for training and the rest were used for testing the cassification perforance of an SVM cassifier. The Radia Basis Function (RBF) was epoyed as an SVM erne. The RBF erne usuay perfors better than other non-inear ernes, such as the poynoia or the sigoid, because it usuay has a better boundary response as it aows for extrapoation, and ost high-diensiona data sets can be approxiated by Gaussian-ie distributions siiar to those used by RBF networs. Moreover it invoves ony one paraeter, which aes it easier to search for its optia paraeters in practice. The second dataset originates fro the Outex database (ontrib_t_00013) and it consists of 68 textures of pixe diensions. Fro each texture 20, sub-iages have been acquired. A tota of 1360 sapes was produced and spit into a baanced training-testing dataset (Ojaa et a., 2002). The distribution has not been quantized, as the SVMs are toerant to the input space diensionaity (Vapni, 1998). The Ms were coputed up to the 3rd order because ony a argina iproveent was observed by the incorporation of higher-order oents. As regards the waveet-based ethods, a 4-eve DWFT was appied for the extraction of WS and WE features, whereas a 1- eve DWFT was appied for W feature extraction. In the atter case the use of ore waveet decoposition eves woud disproportionay increase the coputationa copexity and ead to ony a argina increase in cassification accuracy. Four representative coor spaces, naey the RGB, the I 1 I 2 I 3, the HSV and the IE-Lab have been considered for coor-texture feature extraction. hroaticity oents have ony been extracted fro RGB iages as they inherenty invove the RGB to XYZ coor transforation (Paschos, 2000). The cassification resuts obtained for each dataset are iustrated in Fig. 1 and 2 respectivey. The three eftost couns of these figures present the perforance of the corresponding gray-eve features (,, WE). A ethods produce higher errors for the second dataset, as it is ess diverse (Ojaa et a., 2002). This aso justifies the fact that the Vistex textures were cassified aost perfecty even with the sipe gray-eve features, resuting in ony 0.7% cassification error. Figure 2 shows that in ost cases coor-texture features perfor better than gray-eve features. Actuay, this is not true ony for the Ms, which are outperfored by a other features. However, one coud

4 208 D. K. Iaovidis, D. E. Marouis, and S.A Karanis argue that it ight be preferabe to extract 13 gray-eve waveet energies than 72 waveet-doain coor-texture features (Fig. 3), as the atter provide sighty better perforance than the forer for the cassification of the Outex textures. This cannot be generaized as for the Vistex textures the contribution of coor significanty enhances the perforance of the waveet features. Error (%) WE RGB I1I2I HSV IE-Lab Gray Leve M WS W Figure 1 assification errors obtained for the Vistex textures Error (%) WE RGB I1I2I HSV IE-Lab Gray Leve M WS W Figure 2 assification errors obtained for the Outex textures oparing the perforance of the spatia and features with the perforance of the waveet coor-texture features for both datasets, it coud be concuded that the first ead to coparabe or iproved coor texture cassification accuracy regardess of the coor space epoyed. This iproveent coes with a disproportiona increase of the diensionaity (Fig. 3). Moreover, the contrast feature arginay enhances the accuracy whie by doubing the diension of the feature vector. Different coor spaces see to affect differenty the spatia and the waveet-doain coor-texture feature extraction approaches. More specificay, the features wor we for both RGB and HSV spaces, whie the waveet features are positivey affected by the I 1 I 2 I 3 coor space. The two waveet-based approaches resut in coparabe cassification perforance. Nuber of features WE M WS W Figure 3 Nuber of features feeding the SVMs for the various feature extraction ethods copared 4 ONLUSION We attepted a coparative evauation of spatia and waveet-doain coor-texture features in different coor spaces. The resuts show that coor-texture features coud enhance texture cassification, but there are cases for which gray-eve features coud wor as we. A ajor concusion of this study is that the appropriateness of a coor-texture feature extraction ethod has to be deterined by considering the tradeoff between the accuracy and the feature space diensionaity needs, as these are iposed by a prospective appication. Moreover, the choice of the coor space ay affect the coor texture cassification perforance in soe degree. 5 AKNOWLEDGMENTS This research was funded by the Operationa Progra for Education and Vocationa Training (EPEAEK II) under the fraewor of the project Pythagoras - Support of University Research Groups co-funded by 75% fro the European Socia Fund and by 25% fro nationa funds. REFERENES A-Rawi, M., and Yang, J. (2001) Iuination invariant recognition of coor texture using correation and covariance functions (Eds. Figueiredo M.A.T., Zerubia J., Jain A.K), LNS 2134, pp Dribarean, A., and Whean, P. F. (2001) Experients in coour texture anaysis, Pattern Recognition Letters, Vo. 22, pp , 2001 Iaovidis, D.K., Karanis, S.A., and Marouis, D.E. (2004) oor waveet features for texture recognition under varying iuination using support vector achines, Proceedings of

5 A oparative Study Of oor-texture Iage Features 209 IEEE Internationa onference on Iage Processing (IIP), Singapore, pp Jain, A.K., and Heaey, G. (1998) A utiscae representation incuding opponent coor features for texture recognition, IEEE Trans. on Iage Processing, Vo. 7, No. 1, pp Li, S., Kwo, J.T., Zhu, H., and Wang, Y. (2003) Texture cassification using the support vector achines, Pattern Recognition, Vo. 36, pp Mäenpää, T. (2003) The oca binary pattern approach-extensions and appications PhD Thesis, University of Ouou. Mäenpää, T., and Pietiäinen, M. (2004) assification with coor and texture: Jointy or separatey?, Pattern Recognition, Vo. 37, No. 8, pp Ojaa, T., and Pietiäinen, M. (1998) Unsupervised texture segentation using feature distributions, Pattern Recognition, Vo. 32, pp Ojaa, T., Mäenpää, T., Pietiäinen, M., Viertoa, J., Kynen, J., and Huovinen, S. (2002) Outex - new fraewor for epirica evauation of texture anaysis agoriths, Proceedings of the 16th Internationa onference on Pattern Recognition (IPR), ( Vo. 1, pp Paschos, G. (1998) hroatic orreation Features for Texture Recognition, Pattern Recognition Letters, Vo. 19, pp Paschos, G. (2000) Fast coor texture recognition using chroaticity oents, Pattern Recognition Letters, Vo. 21, pp , Paschos, G. (2001), Perceptuay unifor coor spaces for coor texture anaysis: An epirica evauation, IEEE Transactions on Iage Processing, Vo. 10, No. 6, pp Randen, T., and Husøy, J.H. (1999) Fitering for texture cassification: A coparative study, IEEE Transactions on Pattern Anaysis and Machine Inteigence, Vo. 21, No. 4, pp Unser, M., and Eden, M. (1989) Mutiresoution feature extraction and seection for texture segentation, IEEE Transactions on Pattern Anaysis and Machine Inteigence, Vo. 11, No. 7, pp Van De Wouwer, G., Scheunders, P., Livens, S., and Van Dyc, D. (1999) Waveet correation signatures for coor texture characterization, Pattern Recognition Letters, Vo. 32, pp Vapni, V. P. (1998) Statistica Learning Theory, John Wiey & Sons, New Yor, Wang, L., and Heaey, G. (1998) Using zernie oents for the iuination and geoetry invariant cassification of utispectra texture IEEE Transactions on Iage Processing, Vo. 7, No. 2, pp